arXiv subjects

Ping Chen

Publications and source records attributed to Ping Chen.

At least 55 records · Page 3Linked to original sources

arXiv2026

Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. I: Luminosity Function, Volumetric Rate

We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone.

astro-ph.HE↗

arXiv2026

Fundamental Performance Limits of Non-Coherent ISAC: A Data-Aided Sensing Perspective

In this paper, we investigate a bistatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system over block-fading channels, focusing on the scenario where the sensing and communication receivers (Rxs) are co-located. Under the assumption of unknown channel state information (CSI) at the Rx, two schemes are considered: pilot sensing (PS) and data-aided sensing (DAS). The communication rate-sensing distortion functions for both schemes are characterized. For the DAS scheme, a closed-form asymptotic expression for the sensing distortion is derived by using random matrix theory (RMT). The asymptotic performance analysis explicitly quantifies the significant gains of the DAS scheme, revealing a strict $3$ dB effective SNR improvement in the low-SNR regime and a strictly faster performance scaling rate in the high-SNR limit compared to the PS scheme.

cs.IT↗

arXiv2026

HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models

Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning of large language models, yet most variants target dense architectures. Mixture-of-Experts (MoE) models scale parameters at near-constant per-token compute, and their sparse activation patterns create untapped opportunities for more efficient adaptation. We propose Hot-Experts Layer-level Low-Rank Adaptation (HELLoRA), which attaches LoRA modules only to the most frequently activated experts at each layer. This simple mechanism reduces trainable parameters and adapter-induced FLOPs while improving downstream performance, an effect we attribute to a form of structured regularization that preserves pretrained expert specialization. To stress-test HELLoRA under extreme parameter budgets, we further compose it with LoRI to form HELLoRI, which freezes the up-projection and sparsifies the down-projection. Across three MoE backbones, namely OlMoE-1B-7B, Mixtral-8x7B, and DeepSeekMoE, and three task families covering mathematical reasoning, code generation, and safety alignment, HELLoRA consistently outperforms strong PEFT baselines. Relative to vanilla LoRA on OlMoE, HELLoRA uses 15.7% of the trainable parameters, reduces adapter FLOPs by 38.7%, achieves 1.9x the training throughput, and improves accuracy by 9.2%. On DeepSeekMoE, HELLoRA outperforms LoRA while using only 23.2% of its trainable parameters. These results demonstrate that activation-aware adapter placement is an effective and practical route to scaling PEFT for MoE language models.

cs.LG↗

arXiv2026

PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent knowledge limitations. However, RAG remains vulnerable to poisoning attacks that manipulate retrieved texts to mislead model outputs. Existing defense mechanisms often lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content. In this work, we propose PRA-RAG, a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts. PRA-RAG samples multiple combinations of retrieved texts and utilizes geometric structures in the embedding space to identify a robust subset, from which a stable aggregated representation is derived. We provide theoretical bounds on the maximum impact of poisoned retrieved content and establish a quantitative measure of RAG's robustness. Experiments across multiple benchmarks and RAG architectures demonstrate that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art methods.

cs.IR↗

arXiv2026

ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue

The rapid advancement of Multimodal Large Language Models (MLLMs) has empowered Unmanned Aerial Vehicle (UAV) with exceptional capabilities in spatial reasoning, semantic understanding, and complex decision-making, making them inherently suited for UAV Search and Rescue (SAR). However, existing UAV SAR research is dominated by traditional vision and path-planning methods and lacks a comprehensive and unified benchmark for embodied agents. To bridge this gap, we first propose the novel task of \textbf{Embodied Search and Rescue (ESAR)}, which requires aerial agents to autonomously explore complex environments, identify rescue clues, and reason about victim locations to execute informed decision-making. Additionally, we present \textbf{ESARBench}, the first comprehensive benchmark designed to evaluate MLLM-driven UAV agents in highly realistic SAR scenarios. Leveraging Unreal Engine 5 and AirSim, we construct four high-fidelity, large-scale open environments mapped directly from real-world Geographic Information System (GIS) data to ensure photorealistic landscapes. To rigorously simulate actual rescue operations, our benchmark incorporates dynamic variables including weather conditions, time of day, and stochastic clue placement. Furthermore, we create a dataset of 600 tasks modeled after real-world rescue cases and propose a robust set of evaluation metrics. We evaluate diverse baselines, ranging from traditional heuristics to advanced ground and aerial MLLM-based ObjectNav agents. Experimental results highlight the challenges in ESAR, revealing critical bottlenecks in spatial memory, aerial adaptation, and the trade-off between search efficiency and flight safety. We hope ESARBench serves as a valuable resource to advance research on Embodied Search and Rescue domain. Source code and project page: https://4amgodvzx.github.io/ESAR.github.io.

cs.RO↗

arXiv2026

AT2024wpp: An Extremely Luminous Fast Ultraviolet Transient Powered by Accretion onto a Black Hole

We present the discovery of AT 2024wpp ("Whippet"), a fast and luminous 18cow-like transient. At a redshift of z=0.0868, revealed by Keck Cosmic Web Imager spectroscopy of its faint star-forming host, it is the fourth-nearest example of its class to date. Rapid identification of the source in the Zwicky Transient Facility data stream permitted ultraviolet-through-optical observations to be obtained prior to peak, allowing the first determination of the peak bolometric luminosity (2x10^45 erg/s), maximum photospheric radius (10^15 cm), and total radiated energy (10^51 erg) of an 18cow-like object. We present results from a comprehensive multiwavelength observing campaign, including a far-UV spectrum from the Cosmic Origins Spectrograph on the Hubble Space Telescope and deep imaging extending >100 days post-explosion from the Very Large Telescope, Hubble Space Telescope, Very Large Array, and Atacama Large Millimetre Array. We interpret the observations under a model in which a rapidly-accreting central engine blows a fast (~0.2c) wind into the surrounding medium and irradiates it with X-rays. The high Doppler velocities and intense ionization within this wind prevent identifiable spectroscopic features from appearing in the ejecta or in the surrounding circumstellar material. Weak H and He signatures do emerge in the spectra after 35 days in the form of double-peaked narrow lines. Each peak is individually narrow (full width ~3000 km/s) but the two components are separated by ~6600 km/s, indicating stable structures of denser material, possibly representing streams of tidal ejecta or an ablated companion star.

astro-ph.HE↗

arXiv2026

Classical and spin polarizabilities of singly heavy baryons within heavy baryon chiral perturbation theory

We present a systematic study of the electromagnetic and spin polarizabilities of spin-1/2 singly charmed baryons at $\mathcal{O}(p^4)$ within the framework of heavy baryon chiral perturbation theory. Our results show that the higher-order corrections to the electric polarizability are small, while those to the magnetic polarizability are relatively larger due to the small mass splitting of singly charmed baryons and are closely related to transition magnetic moments. Furthermore, we find that the spin polarizabilities of singly charmed baryons, except for $γ_{M1M1}$, are much smaller than those of the nucleons. We have also calculated the polarizabilities for singly bottom baryons, with the results showing generally larger values than those of singly charmed baryons.

hep-ph↗

arXiv2026

SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress

The widespread deployment of text-to-image diffusion models is significantly challenged by the generation of visually harmful content, such as sexually explicit content, violence, and horror imagery. Common safety interventions, ranging from input filtering to model concept erasure, often suffer from two critical limitations: (1) a severe trade-off between safety and context preservation, where removing unsafe concepts degrades the fidelity of the safe content, and (2) vulnerability to adversarial attacks, where safety mechanisms are easily bypassed. To address these challenges, we propose SafeCtrl, a Region-Aware safety control framework operating on a Detect-Then-Suppress paradigm. Unlike global safety interventions, SafeCtrl first employs an attention-guided Detect module to precisely localize specific risk regions. Subsequently, a localized Suppress module, optimized via image-level Direct Preference Optimization (DPO), neutralizes harmful semantics only within the detected areas, effectively transforming unsafe objects into safe alternatives while leaving the surrounding context intact. Extensive experiments across multiple risk categories demonstrate that SafeCtrl achieves a superior trade-off between safety and fidelity compared to state-of-the-art methods. Crucially, our approach exhibits improved resilience against adversarial prompt attacks, offering a precise and robust solution for responsible generation.

cs.CV↗

arXiv2026

Agentic Flow Steering and Parallel Rollout Search for Spatially Grounded Text-to-Image Generation

Precise Text-to-Image (T2I) generation has achieved great success but is hindered by the limited relational reasoning of static text encoders and the error accumulation in open-loop sampling. Without real-time feedback, initial semantic ambiguities during the Ordinary Differential Equation trajectory inevitably escalate into stochastic deviations from spatial constraints. To bridge this gap, we introduce AFS-Search (Agentic Flow Steering and Parallel Rollout Search), a training-free closed-loop framework built upon FLUX.1-dev. AFS-Search incorporates a training-free closed-loop parallel rollout search and flow steering mechanism, which leverages a Vision-Language Model (VLM) as a semantic critic to diagnose intermediate latents and dynamically steer the velocity field via precise spatial grounding. Complementarily, we formulate T2I generation as a sequential decision-making process, exploring multiple trajectories through lookahead simulations and selecting the optimal path based on VLM-guided rewards. Further, we provide AFS-Search-Pro for higher performance and AFS-Search-Fast for quicker generation. Experimental results show that our AFS-Search-Pro greatly boosts the performance of the original FLUX.1-dev, achieving state-of-the-art results across three different benchmarks. Meanwhile, AFS-Search-Fast also significantly enhances performance while maintaining fast generation speed.

cs.AI↗

arXiv2026

Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning

Diffusion models operate in a reflexive System 1 mode, constrained by a fixed, content-agnostic sampling schedule. This rigidity arises from the curse of state dimensionality, where the combinatorial explosion of possible states in the high-dimensional noise manifold renders explicit trajectory planning intractable and leads to systematic computational misallocation. To address this, we introduce Chain-of-Trajectories (CoTj), a train-free framework enabling System 2 deliberative planning. Central to CoTj is Diffusion DNA, a low-dimensional signature that quantifies per-stage denoising difficulty and serves as a proxy for the high-dimensional state space, allowing us to reformulate sampling as graph planning on a directed acyclic graph. Through a Predict-Plan-Execute paradigm, CoTj dynamically allocates computational effort to the most challenging generative phases. Experiments across multiple generative models demonstrate that CoTj discovers context-aware trajectories, improving output quality and stability while reducing redundant computation. This work establishes a new foundation for resource-aware, planning-based diffusion modeling. The code is available at https://github.com/UnicomAI/CoTj.

cs.LG↗

arXiv2026

IdentityGuard: Context-Aware Restriction and Provenance for Personalized Synthesis

The nature of personalized text-to-image models poses a unique safety challenge that generic context-blind methods are ill-equipped to handle. Such global filters create a dilemma: to prevent misuse, they are forced to damage the model's broader utility by erasing concepts entirely, causing unacceptable collateral damage.Our work presents a more precisely targeted approach, built on the principle that security should be as context-aware as the threat itself, intrinsically bound to the personalized concept. We present IDENTITYGUARD, which realizes this principle through a conditional restriction that blocks harmful content only when combined with the personalized identity, and a concept-specific watermark for precise traceability. Experiments show our approach prevents misuse while preserving the model's utility and enabling robust traceability. By moving beyond blunt, global filters, our work demonstrates a more effective and responsible path toward AI safety.

cs.CR↗

arXiv2026

Mass Production of 2023 KMTNet Microlensing Planets I: Low Mass Ratio

We initiate the systematic search for planets in the 2023 data of the Korea Microlensing Telescope Network (KMTNet), focusing on those planets found by the KMTNet AnomalyFinder with low preliminary estimates of the mass-ratio, $q<2\times 10^{-4}$. The 2023 season is the first for which the photometry of all events was re-reduced prior to the AnomalyFinder search, potentially increasing its sensitivity to planets. We find three strong low-$q$ planet candidates, KMT-2023-BLG-0164 ($q\sim 1.3\times 10^{-4}$), KMT-2023-BLG-1286 ($q\sim 1.9\times 10^{-4}$), and KMT-2023-BLG-1746 ($q\sim 8\times 10^{-5}$). KMT-2023-BLG-0164 is notable in that the source is projected on a very bright ($I=16.0$) foreground star, which is either the planet's host or (more likely) a companion to the host. We obtain a spectrum, finding that its mass and distance are $M\sim 1.0\,M_\odot$ and $D\sim 1.5$ kpc, the latter being the distance of the lens ($D_L$) regardless of whether the spectroscopic target is the host or its companion. We also analyze two other candidates, KMT-2023-BLG-0614 and KMT-2023-BLG-1593, which are unlikely to enter the statistical sample due to their ambiguous interpretations as possible non-planetary events.

astro-ph.EP↗

arXiv2026

MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference

We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantaneous velocity information (e.g., feature caching), which often leads to severe trajectory deviations and error accumulation under high acceleration ratios. MeanCache introduces an average-velocity perspective: by leveraging cached Jacobian--vector products (JVP) to construct interval average velocities from instantaneous velocities, it effectively mitigates local error accumulation. To further improve cache timing and JVP reuse stability, we develop a trajectory-stability scheduling strategy as a practical tool, employing a Peak-Suppressed Shortest Path under budget constraints to determine the schedule. Experiments on FLUX.1, Qwen-Image, and HunyuanVideo demonstrate that MeanCache achieves 4.12X and 4.56X and 3.59X acceleration, respectively, while consistently outperforming state-of-the-art caching baselines in generation quality. We believe this simple yet effective approach provides a new perspective for Flow Matching inference and will inspire further exploration of stability-driven acceleration in commercial-scale generative models.

cs.LG↗

arXiv2026

Beyond Geometry: Artistic Disparity Synthesis for Immersive 2D-to-3D

Current 2D-to-3D conversion methods achieve geometric accuracy but are artistically deficient, failing to replicate the immersive and emotionally resonant experience of professional 3D cinema. This is because geometric reconstruction paradigms mistake deliberate artistic intent, such as strategic zero-plane shifts for pop-out effects and local depth sculpting, for data noise or ambiguity. This paper argues for a new paradigm: Artistic Disparity Synthesis, shifting the goal from physically accurate disparity estimation to artistically coherent disparity synthesis. We propose Art3D, a preliminary framework exploring this paradigm. Art3D uses a dual-path architecture to decouple global depth parameters (macro-intent) from local artistic effects (visual brushstrokes) and learns from professional 3D film data via indirect supervision. We also introduce a preliminary evaluation method to quantify cinematic alignment. Experiments show our approach demonstrates potential in replicating key local out-of-screen effects and aligning with the global depth styles of cinematic 3D content, laying the groundwork for a new class of artistically-driven conversion tools.

cs.CV↗

arXiv2026

RandSet: Randomized Corpus Reduction for Fuzzing Seed Scheduling

Seed explosion is a fundamental problem in fuzzing seed scheduling, where a fuzzer maintains a huge corpus and fails to choose promising seeds. Existing works focus on seed prioritization but still suffer from seed explosion since corpus size remains huge. We tackle this from a new perspective: corpus reduction, i.e., computing a seed corpus subset. However, corpus reduction could lead to poor seed diversity and large runtime overhead. Prior techniques like cull_queue, AFL-Cmin, and MinSet suffer from poor diversity or prohibitive overhead, making them unsuitable for high-frequency seed scheduling. We propose RandSet, a novel randomized corpus reduction technique that reduces corpus size and yields diverse seed selection simultaneously with minimal overhead. Our key insight is introducing randomness into corpus reduction to enjoy two benefits of a randomized algorithm: randomized output (diverse seed selection) and low runtime cost. Specifically, we formulate corpus reduction as a set cover problem and compute a randomized subset covering all features of the entire corpus. We then schedule seeds from this small, randomized subset rather than the entire corpus, effectively mitigating seed explosion. We implement RandSet on three popular fuzzers: AFL++, LibAFL, and Centipede, and evaluate it on standalone programs, FuzzBench, and Magma. Results show RandSet achieves significantly more diverse seed selection than other reduction techniques, with average subset ratios of 4.03% and 5.99% on standalone and FuzzBench programs. RandSet achieves a 16.58% coverage gain on standalone programs and up to 3.57% on FuzzBench in AFL++, triggers up to 7 more ground-truth bugs than the state-of-the-art on Magma, while introducing only 1.17%-3.93% overhead.

cs.SE↗

arXiv2026

A Faint Progenitor System for the Faint Supernova 2024vjm

Type Ia Supernovae (SNe Ia) are well known for their role as standardizable cosmological candles. Their uniformity is credited to their single origin as thermonuclear explosions of White dwarf (WD) stars. Nevertheless, some SNe Ia break this regularity. Prominently, the Iax subclass are less energetic and remarkably diverse, raising questions about their progenitor systems. While no progenitor system of a normal SN Ia has ever been detected, a luminous blue star was identified in pre-explosion images of the site of the bright SN Iax SN 2012Z, suggested to be a helium giant companion star acting as a mass donor to a WD SN progenitor. This is in line with models of weak mass accretion of a WD from a binary companion, producing an explosion that does not fully disrupt the star. However, these models fail to explain the properties of the faintest Type Iax explosions, suggesting either they originate from other WD binary systems, or even from massive progenitor stars. Here, we present the faint SN Iax SN 2024vjm - possibly the faintest supernova observed to date. Using a deep pre-explosion image taken by the recently launched Euclid space mission, we show that its progenitor system must be fainter than the helium giant SN Iax progenitor candidate of SN 2012Z, as well as that of the luminous red companion or remnant of the faint SN 2008ha, and may require a subdwarf helium star as a mass donor. The deep image also provides strong arguments against a massive star origin for this faint supernova. Our observations argue that SN 2024vjm is a WD explosion, but we find that remarkably faint SNe Iax fade more slowly than bright ones, i.e., they evolve in an opposite manner from the famous Phillips relation that makes regular SNe Ia cosmological candles.

astro-ph.HE↗

arXiv2026

Photometry and Spectroscopy of SN 2024pxl: A Luminosity Link Among Type Iax Supernovae

We present extensive ultraviolet to optical photometric and optical to near-infrared (NIR) spectroscopic follow-up observations of the nearby intermediate-luminosity ($M_V = -16.81\pm0.19$~mag) Type Iax supernova (SN) 2024pxl in NGC 6384. SN~2024pxl exhibits a faster light curve than the high-luminosity members of this class, and slower than low-luminosity events. The observationally well-constrained rise time of $\sim$11 days and an estimated synthesized $^{56}$Ni mass of 0.03\, M$_\odot$, based on analytical modeling of the integrated spectral energy distribution light curve, are consistent with models of the weak deflagration of a carbon-oxygen white dwarf. Our optical spectral sequence of SN~2024pxl shows weak \ion{Si}{2} lines and spectral evolution similar to other high-luminosity Type Iax SNe, but also a prominent early-time \ion{C}{2} line, like lower-luminosity Type Iax SNe. The late-time optical spectrum of SN~2024pxl closely matches that of SN~2014dt, and its NIR spectral evolution aligns with that of other well-studied, high-luminosity Type Iax SNe. The spectral-line expansion velocities of SN~2024pxl are at the lower end of the Type Iax SN velocity distribution, and the velocity distribution of iron-group elements compared to intermediate-mass elements suggests that the ejecta are mixed on large scales, as expected in pure deflagration models. SN~2024pxl exhibits characteristics intermediate between those of high-luminosity and low-luminosity Type~Iax SNe, further establishing a link across this diverse class.

astro-ph.HE↗

arXiv2026

APEX: A Decoupled Memory-based Explorer for Asynchronous Aerial Object Goal Navigation

Aerial Object Goal Navigation, a challenging frontier in Embodied AI, requires an Unmanned Aerial Vehicle (UAV) agent to autonomously explore, reason, and identify a specific target using only visual perception and language description. However, existing methods struggle with the memorization of complex spatial representations in aerial environments, reliable and interpretable action decision-making, and inefficient exploration and information gathering. To address these challenges, we introduce \textbf{APEX} (Aerial Parallel Explorer), a novel hierarchical agent designed for efficient exploration and target acquisition in complex aerial settings. APEX is built upon a modular, three-part architecture: 1) Dynamic Spatio-Semantic Mapping Memory, which leverages the zero-shot capability of a Vision-Language Model (VLM) to dynamically construct high-resolution 3D Attraction, Exploration, and Obstacle maps, serving as an interpretable memory mechanism. 2) Action Decision Module, trained with reinforcement learning, which translates this rich spatial understanding into a fine-grained and robust control policy. 3) Target Grounding Module, which employs an open-vocabulary detector to achieve definitive and generalizable target identification. All these components are integrated into a hierarchical, asynchronous, and parallel framework, effectively bypassing the VLM's inference latency and boosting the agent's proactivity in exploration. Extensive experiments show that APEX outperforms the previous state of the art by +4.2\% SR and +2.8\% SPL on challenging UAV-ON benchmarks, demonstrating its superior efficiency and the effectiveness of its hierarchical asynchronous design. Our source code is provided in \href{https://github.com/4amGodvzx/apex}{GitHub}

cs.RO↗